When training a recognition algorithm for point cloud data, a large volume of labeled point cloud data needs to be used as training samples. Therefore, it is necessary to collect a large volume of point cloud data in different scenarios in advance and correctly label the data, such that a smooth optimization process can be guaranteed. In the prior art, the collected point cloud data is generally labeled manually.
However, characteristics presented by objects in a point cloud are often not distinctive, and affected by the ground and other sundries as well. When faced with a large volume of data to be labeled and using manual labeling, the labeling is relatively slow, and objects with similar features may easily be labeled incorrectly.